CrowdRE’26: Celebratory 10th International Workshop on Crowd-Based Requirements Engineering

co-located with the 34th IEEE International Requirements Engineering 2026 conference

https://crowdre.github.io/ws-2026/ 

A Decade of Impact

For ten years, since its inception in 2015, the International Workshop on Crowd-Based Requirements Engineering has shaped how the requirements engineering (RE) community engages with large, diverse user communities. CrowdRE'26 celebrates this milestone by reflecting on what we have achieved and envisioning the path forward. 

The Challenge

Organizations increasingly rely on feedback from thousands to millions of users –through app stores, social media, online forums, and usage data. Yet traditional RE methods struggle to handle the scale, diversity, and complexity of this crowd-generated feedback. CrowdRE has emerged as a paradigm that enables organizations to gather, analyze, and act on user feedback at scale.

New Horizons

As the field evolves, generative AI and large language models (LLMs) are opening unprecedented opportunities—and challenges—for automating feedback analysis and stakeholder engagement. Meanwhile, software engineering increasingly demands responses to cross-cutting qualities such as sustainability, explainability, and ethical AI. 

CrowdRE'26 invites researchers and practitioners to contribute to this evolution, helping shape a roadmap for CrowdRE that extends through the 2030s.

Submissions

CrowdRE seeks submissions containing original research (2–3 pages short; 4–7 pages full; 1-page presentation proposals). Submissions are peer-reviewed by at least three reviewers.

Key Topics

We welcome original research papers addressing all aspects of CrowdRE. This year's workshop centers on two complementary themes:

CROWDRE TEN: The Journey So Far and the Way Forward

As CrowdRE marks a decade of progress, we invite reflections on achievements, unresolved challenges, and future directions. This theme encompasses:

• Contributions of CrowdRE to requirements engineering practice and theory since 2015
• How CrowdRE has shaped understanding of user feedback, the crowd as a stakeholder, and automated requirements elicitation and validation
• Positioning of CrowdRE within the broader RE landscape and adjacent disciplines (data-driven RE, market research, behavioral science, etc.)
• Context and usage data monitoring (beyond text-based feedback) 
• Reliability and trustworthiness of publicly available online user feedback sources 
• Appropriate crowd sizing and composition for different contexts 
• Combating bias, inequality, and underrepresented subgroups in crowds 
• Lessons learned from real-world applications and why certain approaches succeed or fail

CROWDRE TWENTY THIRTY: Toward Improved Quality and Future Impact

Looking ahead to the 2030s, we seek forward-looking work on how CrowdRE can evolve to meet emerging challenges and unlock new opportunities. This theme includes:

• Innovative applications of generative AI, large language models, and deep learning pipelines to gather, filter, and interpret feedback at scale
• Agent-based simulations and AI-driven workflows for automating data collection and analysis
• Neurosymbolic AI, feature models, and ontologies to strengthen classification accuracy
• Novel approaches to handling diverse, voluminous, and heterogeneous user input
• How CrowdRE can identify and address emerging software qualities: sustainability, trustworthy AI, explainability, data privacy, and ethical considerations 
• Data-driven analyses and human-centric frameworks for measuring and improving these qualities at scale 
• Long-term improvement strategies and continuous evolution in crowd-based contexts 
• Alignment with broader societal demands and digital responsibility

More details are available online at https://crowdre.github.io/ws-2026/ 

Committees

Organizing committee

Eduard C. Groen | Oliver Karras | Nitish Patkar | Maria Spichkova

Steering committee

Muneera Bano | Julian Frattini | Eduard C. Groen | Irit Hadar | Oliver Karras | Anas Mahmoud | Norbert Seyff

Program committee

• Sallam Abulhaja, University of Luxembourg
• Sultan Alyahya, King Saud University, SA
• Muneera Bano, CSIRO's Data61, Australia
• Sarmad Bashir, RISE, Sweden
• Parisa Elahidoost, BIT, Sweden
• Farnaz Fotrousi, Chalmers, Sweden
• Davide Fucci, Blekinge Inst. of Tech, Sweden
• Vincenzo Gervasi, University of Pisa, Italy
• Gouri Ginde, University of Calgary, Canada
• Irit Hadar, University of Haifa, Israel
• Rachel Harrison, Oxford Brookes Univ., UK
• Tobias Hey, KIT, Germany
• Mahmood Hosseini, JP Morgan, UK
• Fitsum M. Kifetew, FBK, Italy
• Jil Klünder, FHDW Hannover, Germany
• Meira Levy, Shenkar College, Israel
• Tong Li, Bejing Univ. of Technology, China
• Ze Shi Li, University of Oklahoma, USA
• Soo Ling Lim, UCL, UK & NII, Japan
• Anas Mahmoud, Louisiana State Univ., USA
• Kurt Schneider, Leibniz U. Hannover, Germany
• Alexander Serebrenik, TU/e, Netherlands
• Norbert Seyff, FHNW & UZh, Switzerland
• Chong Wang, Wuhan University, China